13.3.8.12 Graph Embedding Clustering

Chapter Contents (Back)
Graph Embedding.

Yan, S.C.[Shui-Cheng], Xu, D.[Dong], Zhang, B.Y.[Ben-Yu], Zhang, H.J.[Hong-Jiang], Yang, Q.A.[Qi-Ang], Lin, S.,
Graph Embedding and Extensions: A General Framework for Dimensionality Reduction,
PAMI(29), No. 1, January 2007, pp. 40-51.
IEEE DOI 0701
Graph embedding formulation to unify various dimensionality reduction techniques. An intrinsic graph and a penalty graph to implement Marginal Fisher Analysis. Overcome limitations of LDA. BibRef

Yan, S.C.[Shui-Cheng], Xu, D.[Dong], Zhang, B.Y.[Ben-Yu], Zhang, H.J.[Hong-Jiang],
Graph Embedding: A General Framework for Dimensionality Reduction,
CVPR05(II: 830-837).
IEEE DOI 0507
BibRef

Han, L.[Lin], Escolano, F.[Francisco], Hancock, E.R.[Edwin R.], Wilson, R.C.[Richard C.],
Graph characterizations from von Neumann entropy,
PRL(33), No. 15, 1 November 2012, pp. 1958-1967.
Elsevier DOI 1210
BibRef
Earlier: A3, A1, A4, Only:
Information theoretic methods for learning generative models for relational structures,
SIG11(692-693).
IEEE DOI 1201
BibRef
Earlier: A2, A3, Only:
The Mutual Information between Graphs,
ICPR14(94-99)
IEEE DOI 1412
Graph characterizations; Von Neumann entropy; Estrada's heterogeneity index; Thermodynamic depth complexity. Entropy BibRef

Han, L.[Lin], Hancock, E.R.[Edwin R.], Wilson, R.C.[Richard C.],
Characterizing Graphs Using Approximate von Neumann Entropy,
IbPRIA11(484-491).
Springer DOI 1106
BibRef
And:
Entropy versus Heterogeneity for Graphs,
GbRPR11(32-41).
Springer DOI 1105
BibRef
And:
Learning Generative Graph Prototypes Using Simplified von Neumann Entropy,
GbRPR11(42-51).
Springer DOI 1105
BibRef
Earlier: A1, A3, A2:
A Supergraph-based Generative Model,
ICPR10(1566-1569).
IEEE DOI 1008
From supergraph via edit operations. BibRef

Bai, L.[Lu], Hancock, E.R.[Edwin R.],
Graph Kernels from the Jensen-Shannon Divergence,
JMIV(47), No. 1-2, September 2013, pp. 60-69.
WWW Link. 1307
BibRef
And:
A Fast Jensen-Shannon Subgraph Kernel,
CIAP13(I:181-190).
Springer DOI 1311
BibRef
Earlier:
Graph Complexity from the Jensen-Shannon Divergence,
SSSPR12(79-88).
Springer DOI 1211
BibRef
Earlier:
Graph Clustering Using the Jensen-Shannon Kernel,
CAIP11(I: 394-401).
Springer DOI 1109
BibRef

Ye, C.[Cheng], Wilson, R.C.[Richard C.], Hancock, E.R.[Edwin R.],
A Jensen-Shannon Divergence Kernel for Directed Graphs,
SSSPR16(196-206).
Springer DOI 1611
BibRef

Bai, L.[Lu], Rossi, L.[Luca], Cui, L.X.[Li-Xin], Zhang, Z.H.[Zhi-Hong], Ren, P.[Peng], Bai, X.[Xiao], Hancock, E.R.[Edwin R.],
Quantum kernels for unattributed graphs using discrete-time quantum walks,
PRL(87), No. 1, 2017, pp. 96-103.
Elsevier DOI 1703
BibRef
Earlier: A1, A4, A5, A2, A7, Only
An Edge-Based Matching Kernel Through Discrete-Time Quantum Walks,
CIAP15(I:27-38).
Springer DOI 1511
BibRef
And: A1, A2, A5, A4, A7, Only:
A Quantum Jensen-Shannon Graph Kernel Using Discrete-Time Quantum Walks,
GbRPR15(252-261).
Springer DOI 1511
Graph kernels BibRef

Bai, L.[Lu], Zhang, Z.H.[Zhi-Hong], Wang, C.Y.[Chao-Yan], Hancock, E.R.[Edwin R.],
An Edge-Based Matching Kernel for Graphs Through the Directed Line Graphs,
CAIP15(II:85-95).
Springer DOI 1511
BibRef

Bai, L.[Lu], Rossi, L.[Luca], Cui, L.X.[Li-Xin], Hancock, E.R.,
A transitive aligned Weisfeiler-Lehman subtree kernel,
ICPR16(396-401)
IEEE DOI 1705
Convolution, Entropy, Indexes, Kernel, Reliability, Standards, Steady-state BibRef

Bai, L.[Lu], Rossi, L.[Luca], Cui, L.X.[Li-Xin], Hancock, E.R.,
A novel entropy-based graph signature from the average mixing matrix,
ICPR16(1339-1344)
IEEE DOI 1705
Eigenvalues and eigenfunctions, Entropy, Kernel, Laplace equations, Pattern recognition, Probability distribution, Quantum, computing BibRef

Bai, L.[Lu], Rossi, L.[Luca], Torsello, A.[Andrea], Hancock, E.R.[Edwin R.],
A quantum Jensen-Shannon graph kernel for unattributed graphs,
PR(48), No. 2, 2015, pp. 344-355.
Elsevier DOI 1411
BibRef
Earlier: A2, A3, A4, Only:
Manifold Learning and the Quantum Jensen-Shannon Divergence Kernel,
CAIP13(62-69).
Springer DOI 1308
Graph kernels See also Clustering and Embedding Using Commute Times. BibRef

Bai, L.[Lu], Cui, L.X.[Li-Xin], Wang, Y.[Yue], Jin, X.[Xin], Bai, X.[Xiao], Hancock, E.R.,
Shape classification with a vertex clustering graph kernel,
ICPR16(2634-2639)
IEEE DOI 1705
Computer vision, Digital images, Kernel, Shape, Three-dimensional displays, Time complexity, Videos BibRef

Bai, L.[Lu], Ren, P.[Peng], Hancock, E.R.[Edwin R.],
A Hypergraph Kernel from Isomorphism Tests,
ICPR14(3880-3885)
IEEE DOI 1412
Accuracy BibRef
Earlier: A1, A3, A2:
A Jensen-Shannon Kernel for Hypergraphs,
SSSPR12(181-189).
Springer DOI 1211
BibRef
And: A1, A3, A2:
Jensen-Shannon graph kernel using information functionals,
ICPR12(2877-2880).
WWW Link. 1302
BibRef

Bai, L.[Lu], Bunke, H.[Horst], Hancock, E.R.[Edwin R.],
An Attributed Graph Kernel from the Jensen-Shannon Divergence,
ICPR14(88-93)
IEEE DOI 1412
Accuracy BibRef

Bai, L.[Lu], Hancock, E.R.[Edwin R.], Han, L.[Lin],
A Graph Embedding Method Using the Jensen-Shannon Divergence,
CAIP13(102-109).
Springer DOI 1308
BibRef

Zhang, Z.H.[Zhi-Hong], Ren, P.[Peng], Hancock, E.R.[Edwin R.],
Unsupervised Feature Selection Via Hypergraph Embedding,
BMVC12(39).
DOI Link 1301
BibRef

Bai, L.[Lu], Hancock, E.R.[Edwin R.], Han, L.[Lin], Ren, P.[Peng],
Graph clustering using graph entropy complexity traces,
ICPR12(2881-2884).
WWW Link. 1302
BibRef

Bai, L.[Lu], Ren, P.[Peng], Bai, X.[Xiao], Hancock, E.R.[Edwin R.],
A Graph Kernel from the Depth-Based Representation,
SSSPR14(1-11).
Springer DOI 1408
BibRef

Bai, L.[Lu], Hancock, E.R.[Edwin R.],
Fast depth-based subgraph kernels for unattributed graphs,
PR(50), No. 1, 2016, pp. 233-245.
Elsevier DOI 1512
Depth-based representations BibRef

Qiu, H.J.[Huai-Jun], Hancock, E.R.[Edwin R.],
Graph simplification and matching using commute times,
PR(40), No. 10, October 2007, pp. 2874-2889.
WWW Link. 0707
BibRef
Earlier:
Spanning Trees from the Commute Times of Random Walks on Graphs,
ICIAR06(II: 375-385).
Springer DOI 0610
BibRef
And:
Graph Embedding Using Commute Time,
SSPR06(441-449).
Springer DOI 0608
BibRef
And:
Graph Matching using Commute Time Spanning Trees,
ICPR06(III: 1224-1227).
IEEE DOI 0609
BibRef
And: ICPR06(IV: 955).
IEEE DOI 0609
BibRef
And:
Robust Multi-body Motion Tracking Using Commute Time Clustering,
ECCV06(I: 160-173).
Springer DOI 0608
Graph-matching; Graph simplification; Commute time; Graph spectrum BibRef

Bai, L.[Lu], Cui, L.X.[Li-Xin], Escolano, F., Hancock, E.R.[Edwin R.],
An Edge-Based Matching Kernel on Commute-Time Spanning Trees,
ICPR16(2103-2108)
IEEE DOI 1705
Computational complexity, Convolution, Hafnium, Kernel, Pattern matching, Standards BibRef

Qiu, H.J.[Huai-Jun], Hancock, E.R.[Edwin R.],
Clustering and Embedding Using Commute Times,
PAMI(29), No. 11, November 2007, pp. 1873-1890.
IEEE DOI 0711
BibRef
Earlier:
Commute Times, Discrete Green's Functions and Graph Matching,
CIAP05(454-462).
Springer DOI 0509
BibRef
And:
Commute Times for Graph Spectral Clustering,
CAIP05(128).
Springer DOI 0509
See also quantum Jensen-Shannon graph kernel for unattributed graphs, A. BibRef

Robles-Kelly, A.[Antonio], Hancock, E.R.[Edwin R.],
A Riemannian approach to graph embedding,
PR(40), No. 3, March 2007, pp. 1042-1056.
WWW Link. 0611
Graph embedding; Riemannian geometry; Combinatorial Laplacian BibRef

Robles-Kelly, A.[Antonio], Hancock, E.R.[Edwin R.],
Graph Matching using Adjacency Matrix Markov Chains,
BMVC01(Session 5: Matching & Retrieval).
HTML Version. University of York 0110
BibRef

Torsello, A.[Andrea], Hancock, E.R.[Edwin R.],
Graph embedding using tree edit-union,
PR(40), No. 5, May 2007, pp. 1393-1405.
WWW Link. 0702
2D shape; Skeleton; Tree-union; Embedding See also Discovering Shape Classes using Tree Edit-Distance and Pairwise Clustering. BibRef

Torsello, A.[Andrea],
An importance sampling approach to learning structural representations of shape,
CVPR08(1-7).
IEEE DOI 0806
BibRef

Xiao, B.[Bai], Hancock, E.R.[Edwin R.], Wilson, R.C.[Richard C.],
A generative model for graph matching and embedding,
CVIU(113), No. 7, July 2009, pp. 777-789.
Elsevier DOI 0905
BibRef
And: A1, A3, A2:
Quantitative Evaluation on Heat Kernel Permutation Invariants,
SSPR08(217-226).
Springer DOI 0812
BibRef
Earlier: A1, A3, A2:
Object recognition using graph spectral invariants,
ICPR08(1-4).
IEEE DOI 0812
BibRef
And: A2, A3, A1:
Characterising Graphs using the Heat Kernel,
BMVC05(xx-yy).
HTML Version. 0509
BibRef
Earlier: A2, A3, A1:
Graph Clustering using Symmetric Polynomials and Local Linear Embedding,
BMVC03(xx-yy).
HTML Version. 0409
Graph embedding; Shape analysis; Generative model; Heat-kernel analysis BibRef

Xiao, B.[Bai], Hancock, E.R.[Edwin R.], Wilson, R.C.[Richard C.],
Graph characteristics from the heat kernel trace,
PR(42), No. 11, November 2009, pp. 2589-2606.
Elsevier DOI 0907
Heat kernel trace; Graph invariants; Image clustering and recognition BibRef

Xiao, B.[Bai], Hancock, E.R.[Edwin R.], Wilson, R.C.[Richard C.],
Geometric characterization and clustering of graphs using heat kernel embeddings,
IVC(28), No. 6, June 2010, pp. 1003-1021.
Elsevier DOI 1003
Graph spectra; Kernel methods; Graph embedding; Differential geometry; Graph clustering BibRef

Xiao, B.[Bai], Hancock, E.R.[Edwin R.],
A Spectral Generative Model for Graph Structure,
SSPR06(173-181).
Springer DOI 0608
BibRef
Earlier:
Geometric Characterisation of Graphs,
CIAP05(471-478).
Springer DOI 0509
BibRef

Xiao, B.[Bai], Hancock, E.R.[Edwin R.],
Clustering Shapes Using Heat Content Invariants,
ICIP05(I: 1169-1172).
IEEE DOI 0512
BibRef
Earlier:
Graph Clustering Using Heat Content Invariants,
IbPRIA05(II:123).
Springer DOI 0509
BibRef

Xiao, B.[Bai], Hancock, E.R.[Edwin R.],
Trace Formula Analysis of Graphs,
SSPR06(306-313).
Springer DOI 0608
BibRef

Xiao, B.[Bai], Yu, H.[Hang], Hancock, E.R.[Edwin R.],
Graph Matching Using Manifold Embedding,
ICIAR04(I: 352-359).
Springer DOI 0409
BibRef
And:
Graph matching using spectral embedding and alignment,
ICPR04(III: 398-401).
IEEE DOI 0409
BibRef
And:
Graph Matching using Spectral Embedding and Semidefinite Programming,
BMVC04(xx-yy).
HTML Version. 0508
BibRef

Luo, B.[Bin], Wilson, R.C., Hancock, E.R.,
Graph manifolds from spectral polynomials,
ICPR04(III: 402-405).
IEEE DOI 0409
BibRef

Zhao, H.F.[Hai-Feng], Robles-Kelly, A.[Antonio], Zhou, J.[Jun], Lu, J.F.[Jian-Feng], Yang, J.Y.[Jing-Yu],
Graph attribute embedding via Riemannian submersion learning,
CVIU(115), No. 7, July 2011, pp. 962-975.
Elsevier DOI 1106
Graph embedding; Riemannian geometry; Relational matching BibRef

Zhao, H.F.[Hai-Feng], Robles-Kelly, A., Zhou, J.[Jun],
On the Use of the Chi-Squared Distance for the Structured Learning of Graph Embeddings,
DICTA11(422-428).
IEEE DOI 1205
BibRef

Czech, W.W.[Wojciech W.],
Invariants of distance k-graphs for graph embedding,
PRL(33), No. 15, 1 November 2012, pp. 1968-1979.
Elsevier DOI 1210
BibRef
Earlier:
Graph Descriptors from B-Matrix Representation,
GbRPR11(12-21).
Springer DOI 1105
Based on distances between graph vertices. Graph embedding; Graph invariants; B-matrix BibRef

Liu, X., Yan, S., Jin, H.,
Projective Nonnegative Graph Embedding,
IP(19), No. 5, May 2010, pp. 1126-1137.
IEEE DOI 1004
BibRef

Jouili, S.[Salim], Tabbone, S.A.[Salvatore A.],
Hypergraph-based image retrieval for graph-based representation,
PR(45), No. 11, November 2012, pp. 4054-4068.
Elsevier DOI 1206
BibRef
Earlier:
Towards Performance Evaluation of Graph-Based Representation,
GbRPR11(72-81).
Springer DOI 1105
BibRef
Earlier:
Graph Embedding Using Constant Shift Embedding,
ICPR-Contests10(83-92).
Springer DOI 1008
Graph indexing; Graph retrieval; CBIR BibRef

Jouili, S.[Salim], Tabbone, S.A.[Salvatore A.],
Graph Matching Based on Node Signatures,
GbRPR09(154-163).
Springer DOI 0905
BibRef

Jouili, S.[Salim], Tabbone, S.A.[Salvatore A.], Lacroix, V.[Vinciane],
Median Graph Shift: A New Clustering Algorithm for Graph Domain,
ICPR10(950-953).
IEEE DOI 1008
BibRef

Jouili, S.[Salim], Mili, I.[Ines], Tabbone, S.A.[Salvatore A.],
Attributed Graph Matching Using Local Descriptions,
ACIVS09(89-99).
Springer DOI 0909
BibRef

Fu, Y.[Yun], Ma, Y.Q.[Yun-Qian], (Eds.)
Graph Embedding for Pattern Analysis,
Springer2013. ISBN: 978-1-4614-4456-5


WWW Link. 1212
Dimensionality Reduction - Discriminant Analysis - Graph Embedding - Hypergraph - Machine Learning - Manifold Learning - Pattern Recognition - Subspace Learning BibRef

Bao, B.K.[Bing-Kun], Liu, G.C.[Guang-Can], Hong, R.[Richang], Yan, S.C.[Shui-Cheng], Xu, C.S.[Chang-Sheng],
General Subspace Learning With Corrupted Training Data Via Graph Embedding,
IP(22), No. 11, 2013, pp. 4380-4393.
IEEE DOI 1310
computational complexity BibRef

Wacquet, G., Poisson Caillault, É., Hamad, D., Hébert, P.A.,
Constrained spectral embedding for K-way data clustering,
PRL(34), No. 9, July 2013, pp. 1009-1017.
Elsevier DOI 1305
Graph embedding; Spectral clustering; Pairwise constraints; Signed Laplacian BibRef

Hancock, E.R.[Edwin R.], Wilson, R.C.[Richard C.],
Pattern analysis with graphs: Parallel work at Bern and York,
PRL(33), No. 7, 1 May 2012, pp. 833-841.
Elsevier DOI 1203
Award, King Sun Fu, Related. An invited related paper. Graph matching; Edit distance; Graph clustering; Graph embedding BibRef

Sun, C.[Chao], Bao, B.K.[Bing-Kun], Xu, C.S.[Chang-Sheng],
Inductive hierarchical nonnegative graph embedding for 'verb-object' image classification,
MVA(25), No. 7, October 2014, pp. 1647-1659.
Springer DOI 1410
Objects and relations. BibRef

Zhang, H.W.[Han-Wang], Zha, Z.J.[Zheng-Jun], Yang, Y., Yan, S.C.[Shui-Cheng], Chua, T.S.[Tat-Seng],
Robust (Semi) Nonnegative Graph Embedding,
IP(23), No. 7, July 2014, pp. 2996-3012.
IEEE DOI 1407
Image reconstruction BibRef

Zhang, H.W.[Han-Wang], Zha, Z.J.[Zheng-Jun], Yan, S.C.[Shui-Cheng], Wang, M.[Meng], Chua, T.S.[Tat-Seng],
Robust Non-negative Graph Embedding: Towards noisy data, unreliable graphs, and noisy labels,
CVPR12(2464-2471).
IEEE DOI 1208
BibRef

Shi, X., Guo, Z., Lai, Z., Yang, Y., Bao, Z., Zhang, D.,
A Framework of Joint Graph Embedding and Sparse Regression for Dimensionality Reduction,
IP(24), No. 4, April 2015, pp. 1341-1355.
IEEE DOI 1503
Algorithm design and analysis BibRef

Xue, Z.H.[Zhao-Hui], Du, P.J.[Pei-Jun], Li, J.[Jun], Su, H.J.[Hong-Jun],
Simultaneous Sparse Graph Embedding for Hyperspectral Image Classification,
GeoRS(53), No. 11, November 2015, pp. 6114-6133.
IEEE DOI 1509
feature extraction BibRef

Xue, Z.H.[Zhao-Hui], Du, P.J.[Pei-Jun], Li, J.[Jun], Su, H.J.[Hong-Jun],
Sparse Graph Regularization for Hyperspectral Remote Sensing Image Classification,
GeoRS(55), No. 4, April 2017, pp. 2351-2366.
IEEE DOI 1704
geophysical image processing BibRef

Maronidis, A., Tefas, A.[Anastasios], Pitas, I.[Ioannis],
Subclass Graph Embedding and a Marginal Fisher Analysis paradigm,
PR(48), No. 12, 2015, pp. 4024-4035.
Elsevier DOI 1509
Dimensionality reduction BibRef

Iosifidis, A.[Alexandros], Tefas, A.[Anastasios], Pitas, I.[Ioannis],
Sparse extreme learning machine classifier exploiting intrinsic graphs,
PRL(65), No. 1, 2015, pp. 192-196.
Elsevier DOI 1511
Sparse extreme learning machine BibRef

Vretos, N., Tefas, A., Pitas, I.,
A novel dimensionality reduction technique based on kernel optimization through graph embedding,
SIViP(9), No. 1 Supp, December 2015, pp. 3-10.
WWW Link. 1601
BibRef

Huang, S.[Sheng], Yu, Y.[Yang], Yang, D.[Dan], Elgammal, A.M.[Ahmed M.], Yang, D.[Dong],
Collaborative Graph Embedding: A Simple Way to Generally Enhance Subspace Learning Algorithms,
CirSysVideo(26), No. 10, October 2016, pp. 1835-1845.
IEEE DOI 1610
Algorithm design and analysis BibRef

Huang, S.[Sheng], Elgammal, A.E.[Ahmed E.], Yang, D.[Dan],
On the effect of hyperedge weights on hypergraph learning,
IVC(57), No. 1, 2017, pp. 89-101.
Elsevier DOI 1702
Hypergraph learning BibRef

Jian, M.[Meng], Jung, C.[Cheolkon], Zheng, Y.G.[Yao-Guo],
Discriminative Structure Learning for Semantic Concept Detection With Graph Embedding,
MultMed(16), No. 2, February 2014, pp. 413-426.
IEEE DOI 1404
content management BibRef

Jian, M.[Meng], Jung, C.[Cheolkon],
Semi-Supervised Bi-Dictionary Learning for Image Classification With Smooth Representation-Based Label Propagation,
MultMed(18), No. 3, March 2016, pp. 458-473.
IEEE DOI 1603
Bridges BibRef

Vento, M.[Mario],
A long trip in the charming world of graphs for Pattern Recognition,
PR(48), No. 2, 2015, pp. 291-301.
Elsevier DOI 1411
Graph clustering BibRef

Foggia, P.[Pasquale], Vento, M.[Mario],
Graph Embedding for Pattern Recognition,
ICPR-Contests10(75-82).
Springer DOI 1008
BibRef

Chen, Y.L.[Yi-Lei], Hsu, C.T.[Chiou-Ting],
Multilinear Graph Embedding: Representation and Regularization for Images,
IP(23), No. 2, February 2014, pp. 741-754.
IEEE DOI 1402
graph theory BibRef

Mousavi, S.F.[Seyedeh Fatemeh], Safayani, M.[Mehran], Mirzaei, A.[Abdolreza], Bahonar, H.[Hoda],
Hierarchical graph embedding in vector space by graph pyramid,
PR(61), No. 1, 2017, pp. 245-254.
Elsevier DOI 1705
Graph embedding BibRef


Fukui, K., Okuno, A., Shimodaira, H.,
Image and tag retrieval by leveraging image-group links with multi-domain graph embedding,
ICIP16(221-225)
IEEE DOI 1610
Correlation BibRef

Jiménez-Guarneros, M.[Magdiel], Carrasco-Ochoa, J.A.[Jesús Ariel], Martínez-Trinidad, J.F.[José Francisco],
Prototype Selection for Graph Embedding Using Instance Selection,
MCPR15(84-92).
Springer DOI 1506
See also Empirical Study of Oversampling and Undersampling for Instance Selection Methods on Imbalance Datasets, An. BibRef

Aydos, F.[Fahri], Soran, A.[Ahmet], Demirci, M.F.[M. Fatih],
Class Representative Computation Using Graph Embedding,
CIAP13(I:452-461).
Springer DOI 1311
BibRef

Huang, Z.W.[Zhi-Wu], Shan, S.G.[Shi-Guang], Zhang, H.H.[Hai-Hong], Lao, S.H.[Shi-Hong], Chen, X.L.[Xi-Lin],
Cross-view Graph Embedding,
ACCV12(II:770-781).
Springer DOI 1304
face recognition across poses and face recognition across resolutions. BibRef

Olvera-López, J.A.[J. Arturo], Carrasco-Ochoa, J.A.[J. Ariel], Martínez-Trinidad, J.F.[José Francisco],
Prototype Selection Via Prototype Relevance,
CIARP08(153-160).
Springer DOI 0809
BibRef

Yang, J.C.[Jian-Chao], Yang, S.C.[Shui-Cheng], Fu, Y.[Yun], Li, X.L.[Xue-Long], Huang, T.S.[Thomas S.],
Non-negative graph embedding,
CVPR08(1-8).
IEEE DOI 0806
BibRef

Chapter on Matching and Recognition Using Volumes, High Level Vision Techniques, Invariants continues in
Linear Prediction Techniques .


Last update:Sep 25, 2017 at 16:36:46